





import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


_a     :    Dict         =						logging.get_logger(__name__)
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				snake_case   :					List[str]    						=				nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
				assert len(a_   ) == len(a_   ), F"{len(a_   )} != {len(a_   )}"
				dest_layers.load_state_dict(layers_to_copy.state_dict()   )


_a     :    Tuple         =						{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_a     :    Union[str, Any]         =						{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				try:
								snake_case   :					Optional[int]    						=				LAYERS_TO_COPY[n_teacher][n_student]
								return val
				except KeyError:
								if n_student != n_teacher:
												warnings.warn(
												    F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
												    F" {n_student}"   )
								return list(range(a_   )   )
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Optional[Any]:




				"""simple docstring"""






				if n_student > n_teacher:
								raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"   )
				elif n_teacher == n_student:
								return list(range(a_   )   )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def 							a_	(    __magic_name__		,     __magic_name__ = "student"		,     __magic_name__ = None		,     __magic_name__ = None		,     __magic_name__=False		,     __magic_name__=None		,     __magic_name__=None		,     **__magic_name__		,     )				->			Any:




				"""simple docstring"""






				snake_case   :					Optional[int]    						=				"encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
				assert (e is not None) or (d is not None), _msg
				if isinstance(a_		,     a_   ):
								AutoTokenizer.from_pretrained(a_   ).save_pretrained(a_   )  # purely for convenience
								snake_case   :					Tuple    						=				AutoModelForSeqaSeqLM.from_pretrained(a_   ).eval()
				else:
								assert isinstance(a_		,     a_   ), F"teacher must be a model or string got type {type(a_   )}"
				snake_case   :					str    						=				teacher.config.to_diff_dict()

				try:
								snake_case   :					Optional[Any]    						=				teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												snake_case   :					List[str]    						=				teacher_e
								if d is None:
												snake_case   :					List[str]    						=				teacher_d
								init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}   )
				except AttributeError:  # T5
								if hasattr(teacher.config		,     '''num_encoder_layers'''   ):
												snake_case   :					Dict    						=				teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												snake_case   :					List[str]    						=				teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												snake_case   :					str    						=				teacher_e
								if d is None:
												snake_case   :					Tuple    						=				teacher_d
								if hasattr(teacher.config		,     '''num_encoder_layers'''   ):
												init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}   )
								else:
												init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}   )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
				init_kwargs.update(a_   )

				# Copy weights
				snake_case   :					Optional[Any]    						=				teacher.config_class(**a_   )
				snake_case   :					int    						=				AutoModelForSeqaSeqLM.from_config(a_   )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				snake_case   :					Union[str, Any]    						=				student.load_state_dict(teacher.state_dict()		,     strict=a_   )
				assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

				if copy_first_teacher_layers:  # Our copying is done. We just log and save
								snake_case   :					Dict    						=				list(range(a_   )   ), list(range(a_   )   )
								logger.info(
								    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
								    F" {save_path}"   )
								student.save_pretrained(a_   )
								return student, e_layers_to_copy, d_layers_to_copy

				# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
				if e_layers_to_copy is None:
								snake_case   :					List[int]    						=				pick_layers_to_copy(a_		,     a_   )
				if d_layers_to_copy is None:
								snake_case   :					List[int]    						=				pick_layers_to_copy(a_		,     a_   )

				try:
								if hasattr(
								    a_		,     '''prophetnet'''   ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
												copy_layers(teacher.prophetnet.encoder.layers		,     student.prophetnet.encoder.layers		,     a_   )
												copy_layers(teacher.prophetnet.decoder.layers		,     student.prophetnet.decoder.layers		,     a_   )
								else:
												copy_layers(teacher.model.encoder.layers		,     student.model.encoder.layers		,     a_   )
												copy_layers(teacher.model.decoder.layers		,     student.model.decoder.layers		,     a_   )
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block		,     student.encoder.block		,     a_   )
								copy_layers(teacher.decoder.block		,     student.decoder.block		,     a_   )
				logger.info(
				    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"   )
				snake_case   :					Any    						=				{
				    "teacher_type": teacher.config.model_type,
				    "copied_encoder_layers": e_layers_to_copy,
				    "copied_decoder_layers": d_layers_to_copy,
				}
				student.save_pretrained(a_   )
				# Save information about copying for easier reproducibility

				return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
			fire.Fire(create_student_by_copying_alternating_layers)







'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



